| .. _cloudxr-teleoperation-cluster: |
|
|
| Deploying CloudXR Teleoperation on Kubernetes |
| ============================================= |
|
|
| .. currentmodule:: isaaclab |
|
|
| This section explains how to deploy CloudXR Teleoperation for Isaac Lab on a Kubernetes (K8s) cluster. |
|
|
| .. _k8s-system-requirements: |
|
|
| System Requirements |
| ------------------- |
|
|
| * **Minimum requirement**: Kubernetes cluster with a node that has at least 1 NVIDIA RTX PRO 6000 / L40 GPU or equivalent |
| * **Recommended requirement**: Kubernetes cluster with a node that has at least 2 RTX PRO 6000 / L40 GPUs or equivalent |
|
|
| .. note:: |
| If you are using DGX Spark, check `DGX Spark Limitations <https://isaac-sim.github.io/IsaacLab/release/2.3.0/source/setup/installation/index.html#dgx-spark-details-and-limitations>`_ for compatibility. |
|
|
| Software Dependencies |
| --------------------- |
|
|
| * ``kubectl`` on your host computer |
|
|
| * If you use MicroK8s, you already have ``microk8s kubectl`` |
| * Otherwise follow the `official kubectl installation guide <https://kubernetes.io/docs/tasks/tools/#kubectl>`_ |
|
|
| * ``helm`` on your host computer |
|
|
| * If you use MicroK8s, you already have ``microk8s helm`` |
| * Otherwise follow the `official Helm installation guide <https://helm.sh/docs/intro/install/>`_ |
|
|
| * Access to NGC public registry from your Kubernetes cluster, in particular these container images: |
|
|
| * ``https://catalog.ngc.nvidia.com/orgs/nvidia/containers/isaac-lab`` |
| * ``https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cloudxr-runtime`` |
|
|
| * NVIDIA GPU Operator or equivalent installed in your Kubernetes cluster to expose NVIDIA GPUs |
| * NVIDIA Container Toolkit installed on the nodes of your Kubernetes cluster |
|
|
| Preparation |
| ----------- |
|
|
| On your host computer, you should have already configured ``kubectl`` to access your Kubernetes cluster. To validate, run the following command and verify it returns your nodes correctly: |
|
|
| .. code:: bash |
|
|
| kubectl get node |
|
|
| If you are installing this to your own Kubernetes cluster instead of using the setup described in the :ref:`k8s-appendix`, your role in the K8s cluster should have at least the following RBAC permissions: |
|
|
| .. code:: yaml |
|
|
| rules: |
| - apiGroups: [""] |
| resources: ["configmaps"] |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] |
| - apiGroups: ["apps"] |
| resources: ["deployments", "replicasets"] |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] |
| - apiGroups: [""] |
| resources: ["pods"] |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] |
| - apiGroups: [""] |
| resources: ["services"] |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] |
|
|
| .. _k8s-installation: |
|
|
| Installation |
| ------------ |
|
|
| .. note:: |
|
|
| The following steps are verified on a MicroK8s cluster with GPU Operator installed (see configurations in the :ref:`k8s-appendix`). You can configure your own K8s cluster accordingly if you encounter issues. |
|
|
| #. Download the Helm chart from NGC (get your NGC API key based on the `public guide <https://docs.nvidia.com/ngc/ngc-overview/index.html#generating-api-key>`_): |
|
|
| .. code:: bash |
|
|
| helm fetch https://helm.ngc.nvidia.com/nvidia/charts/isaac-lab-teleop-2.3.0.tgz \ |
| --username='$oauthtoken' \ |
| --password=<your-ngc-api-key> |
|
|
| #. Install and run the CloudXR Teleoperation for Isaac Lab pod in the default namespace, consuming all host GPUs: |
|
|
| .. code:: bash |
|
|
| helm upgrade --install hello-isaac-teleop isaac-lab-teleop-2.3.0.tgz \ |
| --set fullnameOverride=hello-isaac-teleop \ |
| --set hostNetwork="true" |
|
|
| .. note:: |
| |
| You can remove the need for host network by creating an external LoadBalancer VIP (e.g., with MetalLB), and setting the environment variable ``NV_CXR_ENDPOINT_IP`` when deploying the Helm chart: |
|
|
| .. code:: yaml |
|
|
| # local_values.yml file example: |
| fullnameOverride: hello-isaac-teleop |
| streamer: |
| extraEnvs: |
| - name: NV_CXR_ENDPOINT_IP |
| value: "<your external LoadBalancer VIP>" |
| - name: ACCEPT_EULA |
| value: "Y" |
|
|
| .. code:: bash |
|
|
| # command |
| helm upgrade --install --values local_values.yml \ |
| hello-isaac-teleop isaac-lab-teleop-2.3.0.tgz |
|
|
| #. Verify the deployment is completed: |
|
|
| .. code:: bash |
|
|
| kubectl wait --for=condition=available --timeout=300s \ |
| deployment/hello-isaac-teleop |
|
|
| After the pod is running, it might take approximately 5-8 minutes to complete loading assets and start streaming. |
|
|
| Uninstallation |
| -------------- |
|
|
| You can uninstall by simply running: |
|
|
| .. code:: bash |
|
|
| helm uninstall hello-isaac-teleop |
|
|
| .. _k8s-appendix: |
|
|
| Appendix: Setting Up a Local K8s Cluster with MicroK8s |
| ------------------------------------------------------ |
|
|
| Your local workstation should have the NVIDIA Container Toolkit and its dependencies installed. Otherwise, the following setup will not work. |
|
|
| Cleaning Up Existing Installations (Optional) |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
|
|
| .. code:: bash |
|
|
| # Clean up the system to ensure we start fresh |
| sudo snap remove microk8s |
| sudo snap remove helm |
| sudo apt-get remove docker-ce docker-ce-cli containerd.io |
| # If you have snap docker installed, remove it as well |
| sudo snap remove docker |
|
|
| Installing MicroK8s |
| ~~~~~~~~~~~~~~~~~~~ |
|
|
| .. code:: bash |
|
|
| sudo snap install microk8s --classic |
|
|
| Installing NVIDIA GPU Operator |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
|
|
| .. code:: bash |
|
|
| microk8s helm repo add nvidia https://helm.ngc.nvidia.com/nvidia |
| microk8s helm repo update |
| microk8s helm install gpu-operator \ |
| -n gpu-operator \ |
| --create-namespace nvidia/gpu-operator \ |
| --set toolkit.env[0].name=CONTAINERD_CONFIG \ |
| --set toolkit.env[0].value=/var/snap/microk8s/current/args/containerd-template.toml \ |
| --set toolkit.env[1].name=CONTAINERD_SOCKET \ |
| --set toolkit.env[1].value=/var/snap/microk8s/common/run/containerd.sock \ |
| --set toolkit.env[2].name=CONTAINERD_RUNTIME_CLASS \ |
| --set toolkit.env[2].value=nvidia \ |
| --set toolkit.env[3].name=CONTAINERD_SET_AS_DEFAULT \ |
| --set-string toolkit.env[3].value=true |
|
|
| .. note:: |
|
|
| If you have configured the GPU operator to use volume mounts for ``DEVICE_LIST_STRATEGY`` on the device plugin and disabled ``ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED`` on the toolkit, this configuration is currently unsupported, as there is no method to ensure the assigned GPU resource is consistently shared between containers of the same pod. |
|
|
| Verifying Installation |
| ~~~~~~~~~~~~~~~~~~~~~~ |
|
|
| Run the following command to verify that all pods are running correctly: |
|
|
| .. code:: bash |
|
|
| microk8s kubectl get pods -n gpu-operator |
|
|
| You should see output similar to: |
|
|
| .. code:: text |
|
|
| NAMESPACE NAME READY STATUS RESTARTS AGE |
| gpu-operator gpu-operator-node-feature-discovery-gc-76dc6664b8-npkdg 1/1 Running 0 77m |
| gpu-operator gpu-operator-node-feature-discovery-master-7d6b448f6d-76fqj 1/1 Running 0 77m |
| gpu-operator gpu-operator-node-feature-discovery-worker-8wr4n 1/1 Running 0 77m |
| gpu-operator gpu-operator-86656466d6-wjqf4 1/1 Running 0 77m |
| gpu-operator nvidia-container-toolkit-daemonset-qffh6 1/1 Running 0 77m |
| gpu-operator nvidia-dcgm-exporter-vcxsf 1/1 Running 0 77m |
| gpu-operator nvidia-cuda-validator-x9qn4 0/1 Completed 0 76m |
| gpu-operator nvidia-device-plugin-daemonset-t4j4k 1/1 Running 0 77m |
| gpu-operator gpu-feature-discovery-8dms9 1/1 Running 0 77m |
| gpu-operator nvidia-operator-validator-gjs9m 1/1 Running 0 77m |
|
|
| Once all pods are running, you can proceed to the :ref:`k8s-installation` section. |
|
|
